superset-mcp

get_or_create_dataset

Retrieve an existing dataset or create it if missing in Apache Superset, using database ID and table name to manage data sources.

Instructions

Get an existing dataset or create it if it doesn't exist.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "database_id": "<database_id>",
      "table_name": "<table_name>"
    },
    "name": "get_or_create_dataset"
  }
}

Input Schema

Name	Required	Description	Default
`database_id`	Yes
`table_name`	Yes
`schema`	No

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Apr 14, 2026

TDQS

B3/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the conditional behavior ('get or create'), but fails to detail critical aspects such as permissions required, whether creation is automatic or requires confirmation, error handling, or what happens if parameters conflict with existing datasets. This is inadequate for a mutation-capable tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, clear sentence that efficiently conveys the core functionality without unnecessary words. It's front-loaded and every part earns its place, making it highly concise and well-structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (conditional get/create with 3 parameters), lack of annotations, and 0% schema coverage, the description is insufficient. While an output schema exists, the description doesn't address parameter meanings, behavioral nuances, or usage distinctions from siblings, leaving significant gaps for an AI agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, meaning parameters are undocumented in the schema. The description adds no information about what 'database_id', 'table_name', or 'schema' represent, their formats, or how they influence the get/create logic. It doesn't compensate for the lack of schema documentation, falling short of the baseline.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Get an existing dataset or create it if it doesn't exist.' It specifies the verb ('get or create') and resource ('dataset'), making the action unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'get_dataset' or 'create_dataset', which would require a 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage context—when you need a dataset and are unsure if it exists—but doesn't provide explicit guidance on when to use this tool versus alternatives like 'get_dataset' or 'create_dataset'. No exclusions or prerequisites are mentioned, leaving room for ambiguity.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.